Time-series satellite remote sensing reveals gradually increasing war damage in the Gaza Strip

ABSTRACT War-related urban destruction is a significant global concern, impacting national security, social stability, people’s survival and economic development. The effects of urban geomorphology and complex geological contexts during conflicts, characterized by different levels of structural damage, are not yet fully understood globally. Here we report how integrating deep learning with data from the independently developed LuoJia3-01 satellite enables near real-time detection of explosions and assessment of different building damage levels in the Israel–Palestine conflict. We found that the damage continually increased from 17 October 2023 to 2 March 2024. We found 3747 missile craters with precision positions and sizes, and timing on vital infrastructure across five governorates in the Gaza Strip on 2 March 2024, providing accurate estimates of potential unexploded ordnance locations and assisting in demining and chemical decontamination. Our findings reveal a significant increase in damage to residential and educational structures, accounting for 58.4% of the total—15.4% destroyed, 18.7% severely damaged, 11.8% moderately damaged and 12.5% slightly damaged—which exacerbates the housing crisis and potential population displacement. Additionally, there is a 34.1% decline in the cultivated area of agricultural land, posing a risk to food security. The LuoJia3-01 satellite data are crucial for impartial conflict monitoring, and our innovative methodology offers a cost-effective, scalable approach to assess future conflicts in various global contexts. These first-time findings highlight the urgent need for an immediate ceasefire to prevent further damage and support the release of hostages and subsequent reconstruction efforts.


Text S2. Data Annotations
Generally, data about land cover and buildings, which are accessible from public sources such as Open Street Map (OSM), can be utilized as annotations for buildings.However, the utility of this data is constrained due to significant registration discrepancies between the buildings and satellite imagery, leading to considerable inaccuracies in the model.Regrettably, the raw post-conflict d ata f rom t he L uoJia3-01 s atellite l acks t he e ssential g round t ruth information required for model training.A practical solution involves training the proposed model on an internationally recognized public dataset on natural disasters, the XBD dataset (available at https://xview2.org/dataset),which includes various types of natural disasters and provides a sample of 98,000 buildings annotated with different levels of damage.The model performed well on the XBD dataset; however, its results were not satisfactory in predicting damage types in an active conflict e nvironment l ike t he I sraeli-Palestinian w ar.T his o utcome was e xpected, g iven t he c omplexity of automated monitoring over time due to the lack of training data and the limited number of damage labels available for conflict a reas, l eading t o p otential m achine l earning o verfitting.No tably, th e XB D da taset di d no t in clude data on wars or complex urban environments like Gaza.
Additionally, the Gaza dataset is complicated by the proximity of buildings, making it challenging to distinguish between individual building's joints and alleys, along with colour variations and similarities.To address this issue, we enhanced our network's capabilities through fine-tuning by c ombining t he X BD d isaster d ataset w ith destruction data from the Gaza war to improve the training process.We initially processed the raw data by dividing it into smaller patches using an image cropping tool.The optimal size of the cropping window is determined by factors such as the size of the buildings and the resolution of the image.The original set of 18,000 images was reduced to 1024 × 1024 pixels.These cropped images were then subjected to a filtering p rocess t o e nhance d ataset q uality, which involved excluding images with significant s moke f rom e xplosions.A fter t his d ata c leansing p rocess, o ur d ataset was composed of 7100 images, each associated with unique geographic coordinates.This curated dataset was employed to train a model using the XBD data, producing mask outputs that identify damaged buildings at the pixel level and categorize the extent of the damage.Given the high cost of annotating the destruction effects, we focused on the North Gaza Governorate, which experienced severe violence and significant d estruction a nd c reated corresponding ground annotations for training.
Damage labels were created as six-channel masks, where channel 0 denotes the background, channel 1 represents destroyed buildings, channel 2 indicates buildings with major damage, channel 3 signifies buildings with moderate damage, channel 4 denotes buildings with minor damage, and channel 5 identifies intact buildings.The complete source code is available on GitHub (https://github.com/DIUx-xView/xView2_baseline)for generating mask outputs and damage predictions.These outputs were initially treated as ground truth for the positive label (building destroyed/severely/partially/slightly damaged).

of 25
Shimaa Holail 1, † , Tamer Saleh 1,2,4, † , Xiongwu Xiao 1,3, †, * , Jing Xiao 4, † , Gui-Song Xia 1,3,4,5 , Zhenfeng Shao To refine accuracy, a manual examination of these outputs was conducted through visual interpretation to ensure a more precise damage assessment, supplemented by building destruction annotations provided by UNOSAT (2).Concurrently, buildings extracted from pre-conflict images were used to create negative data (labelled as intact buildings).These extracted structures were then superimposed onto post-conflict images to assess and categorize building damage, adjusting their classification as needed to accurately reflect the observed damage.The full extent of North Gaza Governorate, with annotations depicting different levels of destruction, and differences between models for damaged areas before and after the manual examination are shown in Fig. S13.The statistical measures of damaged buildings are presented in Table S4.We also performed a validation test using the receiver operating characteristic (ROC) curve in Fig. S14, to demonstrate the advantages of ground truth in model evaluation in conflict areas not included in the training sample.

Text S3. Craters detection model simulation
Within the domain of contemporary conflict analysis, notably in the active Israeli-Palestinian conflict zone, an unreported number of detonated bombs have created craters, alongside a potentially substantial quantity of unexploded ordnance, presenting significant challenges to the environment, infrastructure, and post-conflict demining efforts.The conventional manual approach to crater identification and measurement is labour-intensive and prone to subjective biases.In contrast, automated crater detection methodologies employing artificial intelligence offer a systematic and objective alternative for the identification, mapping, and analytical assessment of craters caused by missile and artillery bombardment.Utilizing high spatial resolution satellite imagery, such as from LuoJia3-01 satellite, we have developed a new deep learning tool tailored to meet the unique demands of analyzing active conflict zones.The potential applications of this tool are manifold, ranging from aiding in the critical task of detecting and removing potential unexploded ordnance to providing detailed insights into the geospatial dynamics of conflict, including soil property assessment and subsurface structure analysis.Our methodology is inspired by the techniques outlined in (3,4), which apply deep learning to autonomously recognize and pinpoint objects of interest.Initially, we utilized the red, green, and blue bands of the visible spectrum as our foundational band combination.Subsequent image correction was conducted using a rational polynomial coefficients (RPC) file, commonly known as a "rpb" file, to orthorectify the satellite images.This orthorectification is crucial for transforming image coordinates into precise ground coordinates.In training our detection algorithm, we employed a diverse array of missile crater images, precisely annotated to encompass a broad spectrum of shapes, sizes, and morphological features (Fig. S10).This variety is essential for developing a detection model that is both robust and generalizable across various contexts.The data was divided into training, validation, and testing sets to facilitate exhaustive experiments and identify the optimal model.For the model input, we utilized images resized to 1024 × 1024 pixels with an average spatial resolution of 0.65 m.Fig. S1 shows the overall architecture of the proposed approach to missile crater detection.We

of 25
Shimaa Holail, Tamer Saleh, Xiongwu Xiao, Jing Xiao, Gui-Song Xia, Zhenfeng Shao, Mi Wang, Jianya Gong, and Deren Li first merge the LuoJia3-01 training dataset after pre-processing.To prevent model over-fitting, data augmentation (5) was applied to the combined training dataset, including random adjustments to the input images, such as horizontal and vertical shifts, rotations, and linear contrast enhancements.Also, the size of the minority class is increased by applying Self-Learning Augmented Segmentation (SLAS) (3).The main difficulty in detecting missile craters in high-resolution remote sensing images is the relatively small size and complex environment of the craters in the images.In this approach, a single-stage detector, S2A-Net (6), is trained on the training set.We chose S2A-Net with compatible convolutional features because it has excellent advantages in producing proposals for small-scale object classes such as cars and fishing boats.To extract features from remote sensing images, the ResNet34 (7) architecture is adopted as the backbone to create the underlying convolutional layers of the Feature Pyramid Network (FPN) (8).
We chose ResNet34 (7) because it is fast and flexible in identifying complex patterns in training data and extrapolating results effectively, while also having a low computational cost.The FPN enhances the standard convolutional network with a top-down pathway and lateral connections so that the network efficiently constructs a rich, multi-scale feature pyramid from a single-resolution input image.Although the traditional FPN excels at integrating higher-level semantic information with the multi-layer feature extraction network, it tends to lose low-level location information.Therefore, we further enhance the features through the path aggregation feature pyramid network (PAFPN) (9), which preserves the precise location information in the underlying features and shortens the information path between the lower layers and the upper features.
After obtaining the detection results, we use non-maximum suppression (NMS) (10) to improve the detection ability of the small missile crater array.Finally, we convert the coordinates of the detected bounding boxes from the rectangular shape (x 1 , y 1 , x 2 , y 2 ) to the circular shape (x, y, r) using the geometric transformation (GT) as in Eq. 1 and obtain the crater detection results. [1] Here, x 1 and y 1 are the coordinates of the top left corner, x 2 and y 2 are the coordinates of the bottom right corner, x and y denote the coordinates of the circle center, and r is the length of the corresponding radius.
We also conducted some comparisons, including training and evaluating crater detection models from satellite images, using backbones such as ResNet34 (7), VGG16 (11), and InceptionV3 (12).Experiments were performed for 30 epochs, and validation results were recorded for each model.The main performance metrics used for evaluation include precision, recall, and F1-score as shown in Table S5.ResNet34 performs better than the other two models.It received the highest F1 scores in both its two conflict images dated November 29, 2023, and January 1, 2024, indicating balanced accuracy and recall.This distinction highlights how flexible ResNet34 is in identifying complex patterns in training data and effectively extrapolating results.As a result, ResNet34 is the preferred model for our specific task because it strikes a balance between high precision, recall, and accuracy and provides insight into deployment decisions that are useful in real-world applications.Employing the finely tuned model, we successfully automated crater detection within the conflict zone and exported the findings in GeoJSON format for seamless integration with the QGIS platform, enabling in-depth spatial analysis.Each detected crater was precisely geolocated, timed, and measured to compile a comprehensive dataset, facilitating inferences about the types of munitions used and the potential locations of unexploded ordnance, thereby assessing their impact on civilian safety throughout the conflict zone.To ensure the utmost accuracy, all detected craters were precisely reviewed to eliminate false positives, which could be mistaken for missile craters due to similarities to shadows, water towers, or remnants from past conflicts.Our methodology signifies a substantial advancement in conflict zone analysis, offering a precise, scalable, and unbiased tool for crater detection and analysis in active conflict zones.This innovative approach not only augments our understanding of conflict's immediate impacts but also contributes to a more profound comprehension of its long-term environmental and infrastructural repercussions, laying a solid groundwork for future research and intervention strategies in analogous global contexts.destroyed buildings using pre-and post-battle images as input.Subsequently, raster predictions are transformed into vector data (R2V).Finally, predictions of various damage states are consolidated into the vector representation of the damage estimates.Recent studies (13,14) have indicated that the Siamese convolutional neural network is an effective method for change detection from satellite imagery.Building upon this insight, we developed a Siamese convolutional neural network with semi-supervised learning, to generate predictions for changing buildings.This network consists of two identical branches with shared weights, enabling the extraction of features from pairs of pre-and post-battle images.A U-Net-shaped segmentation model (15) with a ResNet34 (7) encoder was used as the underlying architecture for the Siamese network and pre-trained using a substantial amount of labelled disaster data.The classification of changes into five distinct damage levels was accomplished by integrating the U-Net with multi-scale feature fusion and cross-directional attention mechanisms (16).The output features from each branch are concatenated, followed by two additional fully convolutional layers.The final layer produces a building damage prediction mask.Implementation of this framework utilized an NVIDIA GRID RTX8000 (28GB) GPU.For post-predictions, the generated predictions undergo filtering through morphology with a window size of 5, and polygons with an area of less than 1 m 2 are removed.The ultimate change prediction is generated by attributing a damage attribute to each vector feature.The damage level for each vector feature is determined by the minimum majority vote of all associated pixels from the change-building prediction mask.If over 50% of the pixels within a vector feature are predicted as a specific damage level, the entire feature is assigned to that level; if there are no prediction values, its damage state is designated as an intact building.To account for the overall quality of predictions provided by the framework in detecting building damage, a manual examination of each possible building state (building destroyed/major damage/moderate/minor damage/intact building) was conducted by scrutinizing LuoJia3-01 satellite images for visibly damaged buildings.Additionally, damage scores from the UNOSAT (2) were employed for validation.

Text S5. Agricultural land changes model simulation
The conflict in Gaza has severely impacted its agricultural lands, introducing numerous detrimental dynamics, such as the destruction of irrigation infrastructure, displacement of labor due to conflict, challenges in marketing crops, bombings, bulldozer levelling, and heavy vehicle activities.Fig. S12 illustrates the extent of damage to agricultural lands caused by the movement of heavy vehicles, tanks, and extensive bulldozing.These factors contribute to negative changes in agricultural land patterns during wartime.Additionally, natural factors like rainfall and temperature variations can further degrade crop health and density.This precarious situation poses a threat to food security, leading to increased dependence on food aid and imports.In our analysis, we focused on the impact of these abnormal dynamics resulting from the ongoing conflict.Since the LuoJia3-01 images only consist of RGB bands, we employed the normalized red-green difference index (NRGDI) (17), a concept previously utilized in precision agriculture using 5 of 25 Shimaa Holail, Tamer Saleh, Xiongwu Xiao, Jing Xiao, Gui-Song Xia, Zhenfeng Shao, Mi Wang, Jianya Gong, and Deren Li RGB bands.The NRGDI is calculated by Eq. 2, using the reflectance values of the red and green bands of the electromagnetic spectrum, which are captured by satellite sensors.Areas showing significant decreases in NRGDI levels indicate regions with damaged agricultural infrastructure, evidenced by lower vegetation density.
Where R is the reflectance in the red band, and G is the reflectance in the green band.Higher NRGDI values indicate healthier vegetation.Conversely, low NRGDI values indicate damaged vegetation.Specifically, we created masks for agricultural land unaffected before the conflict in May 2022 by calculating the NRGDI and applying a threshold hash of 0.15 (https://analytics-library.eiwa.ag/ngrdi-normalized-green-red-difference-index/).Similarly, we created masks of farmland affected by the ongoing conflict on different dates.Next, we calculated the dNRGDI using Eq. 3 by computing the difference between two NRGDI images.This method enables the detection of changes in cultivated areas for annotation purposes without identifying specific crop types.Pixels with dNRGDI levels below a threshold value of -0.05 are classified as damaged, and those above that threshold are classified as undamaged.This threshold was determined by testing various values to find the one that best separates affected areas from unaffected areas.These labels were then used to train a machine-learning model to detect damage to cultivated agricultural lands.The analysis revealed that areas with significant changes in dNRGDI, such as the North and Gaza regions, correspond to active battlefields.Notably, the available images from the LuoJia3-01 satellite allowed for accurate visual interpretation of various land cover types.Training samples were manually collected through visual interpretation of affected and unaffected areas over two years, encompassing the pre-conflict year (2022) and the conflict years (2023-2024).Ultimately, 300,000 pixels were selected to train the model within the SNAP environment offered by the European Space Agency (ESA) (http://step.esa.int/main/).The Random Forest (RF) classification algorithm was chosen for distinguishing between affected and unaffected farmland due to its proven effectiveness in land use land cover classification (https://github.com/samniem/Random-Forest-Land-Classification).The RF classifier underwent training on the high-performance virtual machine of Wuhan University, resulting in the creation of maps between October 2023 and February 2024.The algorithm demonstrated an accuracy of 94.7% in distinguishing between affected and unaffected agricultural lands, verified through validation samples and UNOSAT (18) data.In future work, we plan to build on our existing use of the NRGDI, which has so far only been used to create labels for conflict-affected farmland rather than to identify specific crop types.This initial step is crucial for identifying affected areas.Significant emphasis will be placed on exploring the potential productivity impacts associated with conflict on different crop species and their implications for food security and public health.Shows the extent of damage to agricultural land in a rural area in Khan Yunis Governorate as a result of severe bulldozing and the creation of paths for the movement of heavy machinery and tanks in preparation for the establishment of a buffer zone separating northern Gaza from its center.The damage map was created using the proposed machine learning approach, and the maps were output within the QGIS software package.

Fig. S1 .
Fig. S1.The overall framework of the proposed crater detection approach.

Fig
Fig.S2presents a comprehensive overview of the proposed framework designed for the assessment of battle damage on residential buildings.The framework is comprised of a pre-trained neural network tasked with predicting the state of

Fig. S2 .
Fig. S2.Illustrates an overview of the proposed framework, featuring a U-Net model with a ResNet34 encoder.The process involves feeding the pre-and post-battle images through a Siamese network, where the output features are concatenated before reaching the final convolutional layers.

Fig. S3 .
Fig.S3.Examples of vital landmarks in northern Gaza that were targeted by rocket and artillery shells, resulting in destruction.Buildings that have been damaged are depicted in dark red.The panels illustrate the contours of destroyed landmarks, including the International Eye Hospital, the Gaza Port Pier, the Omar bin Abdul Aziz Educational High School Complex, a house of worship, youth housing towers, and a cemetery that sustained damage.Sources: Google Earth/LuoJia 3-01 Satellite Technologies.Author creations using QGIS 3.32.

Fig. S4 .
Fig. S4.Timeline for detecting missile craters in the northern Gaza area.This chart delineates the number of crater detections alongside their respective sizes.The cumulative number of detections is represented by a red line, whereas the box plots illustrate the varying sizes of craters, indicated by the black values.The blue values beneath the boxes denote the number of detections for each time period.The dotted red line marks the onset of the attacks (October 7, 2023), and the dashed green line indicates the declaration of a truce (spanning 4 days) to facilitate prisoner exchanges between both parties.Green arrows highlight specific instances of craters and breaches in the Israeli wall, as depicted in Fig. S5.Sources: Author calculations.

Fig. S5 .Fig. S6 .Fig. S7 .
Fig. S5.Illustrates examples of missile craters and their impacts on infrastructure.a, illustrates the crossing of an iron fence by Hamas on October 7, 2023.b, depicts significant craters affecting agricultural lands.c, details the complete destruction of the building belonging to the Palestinian Charitable Care Society.d, Damages of Al-Azhar Islamic University.e, describes a severe bomb detonation at Jabalia refugee camp, resulting in a crater comparable in size to a large muddy water pool.Sources: Author creations using QGIS 3.32 based on Google Earth/LuoJia 3-01 Satellite Technologies.

Fig. S8 .
Fig. S8.Presents LuoJia3-01 satellite imagery illustrating the congestion in a section of Rafah city in the southern Gaza Strip, densely populated with tents sheltering displaced individuals.The initial image was captured 18 days following the conflict's onset, whereas the subsequent image was taken approximately four months later.An orange rectangle highlights an area adjacent to agricultural fields that initially appears vacant but is later depicted as a densely populated tent city in the more recent image.Surrounding the temporary shelters for displaced persons are numerous missile craters, with the red rectangle indicating areas where civilian casualties may have occurred.Sources: Author creations using QGIS 3.32 based on LuoJia3-01 satellite imagery.

Fig. S9 .
Fig. S9.Comparison of the numbers of missile craters detected in urban and non-urban areas.Eighty-four missile crater samples were selected, and the boundaries of each sample were manually delineated based on LuoJia3-01 satellite imagery.Boxplots represent the average number of missile craters during different periods in urban (n = 605 pixels) and non-urban areas (n = 8,352 pixels).Comparative analysis was performed using Welch's t-test (P < 0.001) at a 95% confidence level, indicating a significant difference in the number of craters between the two samples.The bar in the middle of the boxplot represents the median value, while the upper and lower bounds represent the first and third quartiles, respectively; the whiskers indicate the minimum and maximum values.The boxplots demonstrate that densely populated areas exhibit a higher mean and greater variance in the number of missile craters compared to less populated areas.Sources: Author calculations.

Fig. S10 .
Fig. S10.Examples of missile craters and their corresponding masks in the training dataset.The first and second rows display various sizes of precisely annotated craters.The third-row presents, from left to right, the original RGB image, the uncertainty map, and the detected craters.Category labels are as follows: black for the background and grey for padding.

Fig. S11 .
Fig. S11.Correlation matrix between different damage levels.Colors range from dark red (high positive correlation) to light red/pink (lower positive correlation).There are strong correlations between the first three damage categories, indicating a consistent assessment across these levels, while intact structures show a significantly lower correlation with the damaged categories, indicating a clear distinction in the dataset between damaged and undamaged buildings.

Fig. S12.
Fig.S12.Shows the extent of damage to agricultural land in a rural area in Khan Yunis Governorate as a result of severe bulldozing and the creation of paths for the movement of heavy machinery and tanks in preparation for the establishment of a buffer zone separating northern Gaza from its center.The damage map was created using the proposed machine learning approach, and the maps were output within the QGIS software package.

Fig. S13 .
Fig. S13.The labels of the North Gaza Governorate on November 29, 2023.Red indicates destruction, orange indicates major damage, blue indicates minor damage, and green indicates no damage.The yellow rectangle includes a populated residential area in Beit Hanoun under analysis, which was heavily damaged, as shown in the zoomed-in images in (b-d).Panels (b) and (c) show the ground truth damage annotations before and after manual examination, respectively.Panel (d) shows the UNOSAT damage annotations, providing an additional layer of verification.The automated model may classify a building as slightly damaged, but after manual examination, it may be identified as undamaged by noting the entire structure that the model analysis neglects, as shown by the green color in panel (c).We can observe that the damage annotations points indicated in red by UNOSAT consistent with the pre-and post-manual examination model with slight differences, as shown in the cyan rectangle in panel (d).Source: Mapping conducted by the author using QGIS 3.32 and LuoJia3-01 satellite imagery.

Fig. S14 .
Fig. S14.ROC curves before and after manual examination in portions of the conflict area not included in the training sample, specifically within the Khan Yunis Governorate in Gaza.The performance depicted represents 25% of the test sample used for validation.The proposed model, following manual examination, achieves a high accuracy score with an AUC of 0.91, significantly improving upon the pre-manual examination AUC of 0.79.This enhancement yields practically feasible destruction data.Sources: Author calculations.